{"id":"https://openalex.org/W2572655910","doi":"https://doi.org/10.1109/vcip.2016.7805451","title":"Cross-modal face matching: Tackling visual abstraction using fine-grained attributes","display_name":"Cross-modal face matching: Tackling visual abstraction using fine-grained attributes","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2572655910","doi":"https://doi.org/10.1109/vcip.2016.7805451","mag":"2572655910"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2016.7805451","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2016.7805451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040260907","display_name":"Yichuan Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yichuan Hu","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telcommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telcommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343505","display_name":"Ke Li","orcid":"https://orcid.org/0000-0002-6496-6643"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Li","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telcommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telcommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100626771","display_name":"Honggang Zhang","orcid":"https://orcid.org/0000-0001-8287-6783"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Honggang Zhang","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telcommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telcommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5040260907"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.334,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6840586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9711999893188477,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9689000248908997,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8281164169311523},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.7297289967536926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6367908120155334},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.6305942535400391},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5895575881004333},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5883561372756958},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5575742721557617},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5520994663238525},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5134085416793823},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5075801610946655},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47453486919403076},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.464722216129303},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.4440206289291382},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3404306173324585},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06608378887176514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8281164169311523},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.7297289967536926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6367908120155334},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.6305942535400391},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5895575881004333},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5883561372756958},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5575742721557617},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5520994663238525},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5134085416793823},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5075801610946655},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47453486919403076},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.464722216129303},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.4440206289291382},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3404306173324585},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06608378887176514},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip.2016.7805451","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2016.7805451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1425104224","https://openalex.org/W1699277040","https://openalex.org/W1990937109","https://openalex.org/W1993935870","https://openalex.org/W2015361919","https://openalex.org/W2015635574","https://openalex.org/W2052094668","https://openalex.org/W2088009615","https://openalex.org/W2106277773","https://openalex.org/W2131246314","https://openalex.org/W2134270519","https://openalex.org/W2158096215","https://openalex.org/W2161533145","https://openalex.org/W2185498755","https://openalex.org/W2186645503","https://openalex.org/W2435136795","https://openalex.org/W2536626143","https://openalex.org/W2949648946","https://openalex.org/W3143107425","https://openalex.org/W6628412846","https://openalex.org/W6672287675","https://openalex.org/W6686748215"],"related_works":["https://openalex.org/W4388870064","https://openalex.org/W2210139803","https://openalex.org/W4235186151","https://openalex.org/W2054685365","https://openalex.org/W2056057048","https://openalex.org/W2667588871","https://openalex.org/W2272354214","https://openalex.org/W2084768720","https://openalex.org/W2043010663","https://openalex.org/W4248308508"],"abstract_inverted_index":{"Despite":[0],"great":[1],"strides":[2],"made":[3],"in":[4,40,74,120],"facial":[5,12,84,150],"verification,":[6],"it":[7],"remains":[8],"challenging":[9],"to":[10,21,61,138,156],"match":[11],"images":[13],"across":[14],"different":[15],"modalities.":[16],"This":[17],"is":[18],"mainly":[19],"due":[20,60],"the":[22,36,62,91,111,190,193],"cross-modal":[23,64,123,195],"gap":[24,65,114],"induced":[25],"by":[26,67,88,98,197],"feature":[27,37,109,159],"heterogeneity.":[28],"Much":[29],"prior":[30],"work":[31],"had":[32,70],"focused":[33],"on":[34,49,82,199],"bridging":[35],"gap,":[38],"resulting":[39,119],"near-perfect":[41],"matching":[42,50,83,124],"accuracies":[43],"for":[44],"viewed":[45],"sketches.":[46],"Nonetheless,":[47],"studies":[48],"unviewed":[51],"(forensic)":[52],"sketches":[53],"and":[54,144,161,164,184],"caricatures,":[55],"a":[56,100,131,168],"much":[57],"harder":[58],"problem":[59],"additional":[63],"introduced":[66],"visual":[68,92,104,112,186],"abstraction,":[69],"only":[71],"just":[72],"commenced":[73],"recent":[75,201],"years.":[76],"In":[77],"this":[78],"paper,":[79],"we":[80,129,146,166],"focus":[81],"caricatures":[85],"with":[86,106],"photos":[87],"directly":[89],"addressing":[90],"abstraction":[93,113],"problem.":[94],"We":[95,188],"show":[96,167],"that":[97,179],"synergizing":[99],"taxonomy":[101],"of":[102,192],"fine-grained":[103,140,185],"attributes":[105,141],"part-aware":[107],"low-level":[108,182],"extraction,":[110],"can":[115,152,171],"be":[116,153,172],"effectively":[117],"traversed,":[118],"improved":[121],"overall":[122],"accuracy.":[125],"More":[126],"specifically,":[127],"(i)":[128],"propose":[130],"simple":[132],"yet":[133],"effective":[134],"geometry-based":[135],"attribute":[136,162],"classifier":[137],"detect":[139],"at":[142],"part-level,":[143],"(ii)":[145],"demonstrate":[147,189],"how":[148],"meaningful":[149],"regions":[151],"reliably":[154],"detected":[155],"enable":[157],"localized":[158],"extraction":[160],"detection,":[163],"(iii)":[165],"common":[169],"embedding":[170],"learned":[173],"using":[174],"Canonical":[175],"Correlation":[176],"Analysis":[177],"(CCA)":[178],"combines":[180],"part-based":[181],"features":[183],"attributes.":[187],"superiority":[191],"proposed":[194],"strategy":[196],"evaluating":[198],"two":[200],"photo-caricature":[202],"datasets.":[203]},"counts_by_year":[{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
